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README.md

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+---
+language: trv
+language_name: Taroko
+language_family: austronesian_formosan
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-austronesian_formosan
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 3.923
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7817
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-11
+---
+
+# Taroko - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Taroko** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.409x | 3.41 | 0.1717% | 804,137 |
+| **16k** | 3.644x | 3.65 | 0.1835% | 752,396 |
+| **32k** | 3.786x | 3.79 | 0.1907% | 724,248 |
+| **64k** | 3.923x 🏆 | 3.92 | 0.1976% | 698,951 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Nlixan （丟棄的線） EX:smeli naq ware puto sneqic nlixan bubu na ka laqi mqedin. Pnyah...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁n lixan ▁( 丟 棄 的 線 ) ▁ex : ... (+22 more)` | 32 |
+| 16k | `▁nlixan ▁( 丟 棄 的 線 ) ▁ex : sme ... (+19 more)` | 29 |
+| 32k | `▁nlixan ▁( 丟 棄 的 線 ) ▁ex : smeli ... (+17 more)` | 27 |
+| 64k | `▁nlixan ▁( 丟棄的線 ) ▁ex : smeli ▁naq ▁ware ▁puto ... (+14 more)` | 24 |
+
+**Sample 2:** `Empprngaw kari(溝通、談話) Yaku ni bubu mu, empprngaw kari han!`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁empprngaw ▁kari ( 溝 通 、 談 話 ) ▁yaku ... (+8 more)` | 18 |
+| 16k | `▁empprngaw ▁kari ( 溝 通 、 談話 ) ▁yaku ▁ni ... (+7 more)` | 17 |
+| 32k | `▁empprngaw ▁kari ( 溝 通 、 談話 ) ▁yaku ▁ni ... (+7 more)` | 17 |
+| 64k | `▁empprngaw ▁kari ( 溝通 、 談話 ) ▁yaku ▁ni ▁bubu ... (+6 more)` | 16 |
+
+**Sample 3:** `縮圖|Reynhekwo , Switzerland Reynhekwo / Renhokuo (聯合國): 193個國`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁縮圖 | reyn he kwo ▁, ▁s wit zer land ... (+19 more)` | 29 |
+| 16k | `▁縮圖 | reyn hekwo ▁, ▁switzerland ▁reyn hekwo ▁/ ▁ren ... (+11 more)` | 21 |
+| 32k | `▁縮圖 | reyn hekwo ▁, ▁switzerland ▁reynhekwo ▁/ ▁ren hokuo ... (+9 more)` | 19 |
+| 64k | `▁縮圖 | reynhekwo ▁, ▁switzerland ▁reynhekwo ▁/ ▁renhokuo ▁( 聯合國 ... (+6 more)` | 16 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 3.923x compression
+- **Lowest UNK Rate:** 8k with 0.1717% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 7,668 | 12.90 | 19,784 | 17.2% | 43.1% |
+| **2-gram** | Subword | 262 🏆 | 8.03 | 4,696 | 69.8% | 98.6% |
+| **3-gram** | Word | 8,123 | 12.99 | 21,493 | 20.5% | 40.6% |
+| **3-gram** | Subword | 1,934 | 10.92 | 22,856 | 31.5% | 73.9% |
+| **4-gram** | Word | 13,253 | 13.69 | 36,605 | 20.8% | 34.4% |
+| **4-gram** | Subword | 9,420 | 13.20 | 96,381 | 16.1% | 45.8% |
+| **5-gram** | Word | 9,241 | 13.17 | 26,596 | 23.7% | 37.8% |
+| **5-gram** | Subword | 28,374 | 14.79 | 203,086 | 10.5% | 30.9% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `kiya ka` | 2,292 |
+| 2 | `kana ka` | 1,899 |
+| 3 | `seejiq o` | 1,657 |
+| 4 | `tnpusu seejiq` | 1,508 |
+| 5 | `o mangal` | 1,468 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tnpusu seejiq o` | 1,449 |
+| 2 | `seejiq o mangal` | 1,444 |
+| 3 | `pnyahan pnatas 參考資料` | 1,005 |
+| 4 | `hiyi ka kana` | 723 |
+| 5 | `sapah ka kneegu` | 722 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `tnpusu seejiq o mangal` | 1,443 |
+| 2 | `hiyi tnpusu seejiq o` | 722 |
+| 3 | `na hiyi tnpusu seejiq` | 722 |
+| 4 | `sapah ka kneegu na` | 722 |
+| 5 | `ka kneegu na sapah` | 722 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ka kana knhbragan na hiyi` | 722 |
+| 2 | `sapah ka kneegu na sapah` | 722 |
+| 3 | `kana knhbragan na hiyi tnpusu` | 722 |
+| 4 | `knhbragan na hiyi tnpusu seejiq` | 722 |
+| 5 | `na hiyi tnpusu seejiq o` | 722 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a _` | 159,040 |
+| 2 | `a n` | 151,698 |
+| 3 | `_ k` | 114,078 |
+| 4 | `n g` | 106,714 |
+| 5 | `n _` | 93,857 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a n _` | 72,949 |
+| 2 | `_ k a` | 53,488 |
+| 3 | `k a _` | 48,276 |
+| 4 | `a n g` | 38,914 |
+| 5 | `n g _` | 36,466 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ k a _` | 38,804 |
+| 2 | `a n g _` | 18,270 |
+| 3 | `g a n _` | 15,177 |
+| 4 | `_ n a _` | 14,291 |
+| 5 | `a l a n` | 13,651 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `a l a n g` | 11,226 |
+| 2 | `i q a n _` | 10,533 |
+| 3 | `n i q a n` | 10,125 |
+| 4 | `k a w a s` | 10,012 |
+| 5 | `l a n g _` | 9,914 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 262
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~31% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.7260 | 1.654 | 5.46 | 66,965 | 27.4% |
+| **1** | Subword | 1.3366 | 2.526 | 8.22 | 3,648 | 0.0% |
+| **2** | Word | 0.2882 | 1.221 | 1.68 | 365,251 | 71.2% |
+| **2** | Subword | 0.4317 | 1.349 | 2.61 | 29,967 | 56.8% |
+| **3** | Word | 0.0872 | 1.062 | 1.14 | 612,144 | 91.3% |
+| **3** | Subword | 0.4820 | 1.397 | 2.65 | 78,261 | 51.8% |
+| **4** | Word | 0.0266 🏆 | 1.019 | 1.04 | 698,380 | 97.3% |
+| **4** | Subword | 0.4748 | 1.390 | 2.26 | 207,257 | 52.5% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `ka tucay cungcen di ririh tan paah baraw o kndadax cu prajil pusa kari qpruhan nii`
+2. `na lxanan waya mi kingal hngkawas na sin ing wen hwa 文化 pusu nniqan hiya han`
+3. `o nirih na bukung klwaan cing ci pnaah hngkawas mnda kingal alang icil so niyi bungka`
+
+**Context Size 2:**
+
+1. `kiya ka kiya ni nii lhbun bi dgiyaq kana ki wada paru bale qqtaun quri kesun yisu`
+2. `kana ka snluan ruwan klwaan dnii ga ida niqan ka sediq kiya knkana dapa lmiqu mi ccamac`
+3. `seejiq o mangal 2 niqan 2 paru nniqan rnaaw ni ungat bi knsyangan ni niqan kingal ka`
+
+**Context Size 3:**
+
+1. `tnpusu seejiq o mangal 88 niqan 2 609 hiyi sp rahuq na uxay tnpusu seejiq o mangal 80`
+2. `seejiq o mangal 83 niqan 1 347 hiyi koia kana ka kleegan seejiq ga ni rahuq na o4`
+3. `pnyahan pnatas 參考資料 內政部戶政司全球資訊網 原住民族委員會全球資訊網統計資料 hangan alang 部落名稱 alang qnagan tukubeycu na alang 部...`
+
+**Context Size 4:**
+
+1. `tnpusu seejiq o mangal 6 niqan 7 hiyi koia kana ka kleegan seejiq ga ni rahuq na o1 pusupnyahan`
+2. `hiyi tnpusu seejiq o mangal 97 niqan 331 hiyi sp rahuq na uxay tnpusu seejiq o mangal 41 niqan`
+3. `hiyi ka kana knhbragan na hiyi tnpusu seejiq o mangal 75 niqan 779 hiyi koia kana ka kleegan seejiq`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_syrísna_po,_kng`
+2. `ac_msun_musey_2_`
+3. `n_c_mtax.】_hmiy-`
+
+**Context Size 2:**
+
+1. `a_mri_mdada_tru.s`
+2. `angcin),_mqnhban_`
+3. `_ki_mi_kapah_do_2`
+
+**Context Size 3:**
+
+1. `an_hiya_mpdaun_seu`
+2. `_kanana_bale_meran`
+3. `ka_uri,_beran_riyu`
+
+**Context Size 4:**
+
+1. `_ka_hmrinas_ka_daw,`
+2. `ang_mkbrnux_na_skde`
+3. `gan_kasi_ka_waso_ni`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (207,257 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 26,300 |
+| Total Tokens | 761,987 |
+| Mean Frequency | 28.97 |
+| Median Frequency | 3 |
+| Frequency Std Dev | 343.10 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ka | 39,083 |
+| 2 | na | 16,339 |
+| 3 | o | 12,805 |
+| 4 | alang | 9,788 |
+| 5 | ni | 8,476 |
+| 6 | u | 8,051 |
+| 7 | niqan | 7,350 |
+| 8 | mi | 6,845 |
+| 9 | kiya | 6,666 |
+| 10 | dha | 6,542 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | ptaqi | 2 |
+| 2 | kiyang | 2 |
+| 3 | skyidaw | 2 |
+| 4 | qbrus | 2 |
+| 5 | mnurax | 2 |
+| 6 | kmawah | 2 |
+| 7 | beydat | 2 |
+| 8 | mjilux | 2 |
+| 9 | 衣物等 | 2 |
+| 10 | mpggaalu | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.2169 |
+| R² (Goodness of Fit) | 0.992292 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 43.4% |
+| Top 1,000 | 73.8% |
+| Top 5,000 | 89.7% |
+| Top 10,000 | 94.4% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9923 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 43.4% of corpus
+- **Long Tail:** 16,300 words needed for remaining 5.6% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7817 | 0.3299 | N/A | N/A |
+| **mono_64d** | 64 | 0.5768 | 0.2955 | N/A | N/A |
+| **mono_128d** | 128 | 0.1326 | 0.2814 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7817 🏆 | 0.3225 | 0.0220 | 0.1500 |
+| **aligned_64d** | 64 | 0.5768 | 0.2983 | 0.0320 | 0.2400 |
+| **aligned_128d** | 128 | 0.1326 | 0.2761 | 0.0640 | 0.2760 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7817 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3006. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 6.4% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **0.223** | High formulaic/idiomatic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-s` | syawswocya, ssikun, sulu |
+| `-m` | mbomou, mhiyang, mrunu |
+| `-p` | psnaqun, philippine, psuung |
+| `-t` | tyencucyaw, tmbawa, taha |
+| `-k` | kayi, kntruma, kwose |
+| `-c` | cyapiar, cyupin, cyuan |
+| `-h` | hngakan, hwami, hnridan |
+| `-n` | ncyaropihay, nga, nrihan |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-n` | qhdin, yican, hngakan |
+| `-an` | yican, hngakan, dmatan |
+| `-ng` | mhiyang, mkgarang, 1alang |
+| `-g` | mhiyang, mkgarang, 1alang |
+| `-a` | nga, syawswocya, tmbawa |
+| `-u` | mbomou, mrunu, sulu |
+| `-y` | ncyaropihay, aripay, amnesty |
+| `-i` | kayi, yami, hwami |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `uwan` | 1.92x | 113 contexts | tuwan, luwan, kuwan |
+| `iyan` | 1.68x | 106 contexts | siyan, kiyan, diyan |
+| `atas` | 2.25x | 22 contexts | matas, patas, natas |
+| `inga` | 1.58x | 78 contexts | ingal, kinga, pingan |
+| `eeji` | 2.41x | 16 contexts | seeji, seejia, seejiq |
+| `ngal` | 1.55x | 74 contexts | mngal, ingal, ngala |
+| `anga` | 1.34x | 137 contexts | manga, hanga, angal |
+| `ahan` | 1.42x | 95 contexts | tahan, qahan, wahan |
+| `seej` | 2.41x | 13 contexts | seeji, seejia, seejiq |
+| `alay` | 1.96x | 22 contexts | balay, malay, lalay |
+| `waan` | 2.00x | 20 contexts | rwaan, hwaan, kwaan |
+| `lwaa` | 2.31x | 11 contexts | klwaam, klwaan, qlwaan |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-p` | `-n` | 226 words | prilan, ptasun |
+| `-s` | `-n` | 170 words | snhian, snluun |
+| `-p` | `-an` | 170 words | prilan, ppaan |
+| `-k` | `-n` | 135 words | kalibuan, kyrgyazstan |
+| `-k` | `-an` | 108 words | kalibuan, kyrgyazstan |
+| `-s` | `-an` | 107 words | snhian, snyusan |
+| `-t` | `-n` | 107 words | tnegjyalan, tetun |
+| `-c` | `-n` | 90 words | cangmyeyn, cungcgn |
+| `-c` | `-ng` | 82 words | cucngtang, cinghung |
+| `-c` | `-g` | 82 words | cucngtang, cinghung |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| syeyncing | **`syeync-i-ng`** | 7.5 | `i` |
+| taypinyan | **`taypin-y-an`** | 7.5 | `y` |
+| kongciyun | **`kongci-y-un`** | 7.5 | `y` |
+| phdeyngki | **`p-h-deyngki`** | 7.5 | `deyngki` |
+| sunghosay | **`sungho-s-ay`** | 7.5 | `s` |
+| niyawcwey | **`niyawc-w-ey`** | 7.5 | `w` |
+| mingcutan | **`mingcu-t-an`** | 7.5 | `t` |
+| tyeynsing | **`tyeyns-i-ng`** | 7.5 | `i` |
+| pncubuwan | **`pn-cu-buwan`** | 7.5 | `buwan` |
+| mincucuyi | **`mincucu-y-i`** | 7.5 | `y` |
+| teynckung | **`teynck-u-ng`** | 7.5 | `u` |
+| yueynsuay | **`yueyns-u-ay`** | 7.5 | `u` |
+| pnkbrihan | **`pn-k-brihan`** | 7.5 | `brihan` |
+| hwangcuyey | **`hwangcu-y-ey`** | 7.5 | `y` |
+| peyruskeni | **`peyruske-n-i`** | 7.5 | `n` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Taroko shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (3.92x) |
+| N-gram | **2-gram** | Lowest perplexity (262) |
+| Markov | **Context-4** | Highest predictability (97.3%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-11 01:38:25*

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